 In recent years, there has been a shift towards using alphabetic data instead of real-valued data in statistical inference. This shift is motivated by the development of data science, which has led to increased use of alphabetic data in many applications. However, statistical inference based on alphabetic data is less well understood than traditional statistical inference based on real-valued data. This paper introduces some basic concepts related to entropy-based statistical inference, such as entropic sample spaces, entropic distributions, entropic statistics, and entropic moments. It also discusses how these concepts can be used to develop a sound framework for rigorously developing entropy-based statistical exercises. This article is authored by Xi Jinping.